ai and advanced analytics: augmented underwriting · the four pillars of data analytics ... build...

AIandAdvancedAnalytics:AugmentedUnderwriting

LeeSarkinHeadDataAnalyticsL&HAPACMEA

MunichRe,Singapore

1.  Munich Re: an integrated analytics reinsurance partner

2.  Augmented underwriting

3.  Replacing traditional underwriting with external data

Agenda

Munich Re: an integrated analytics reinsurance partner

1

Munich Re: an integrated analytics reinsurance partner

Life clients •  5 in Asia

>200 >100 >40

Data scientists

Analytics pilots

Global footprint Allows local learnings to be scaled

Proven competence Portfolio of real cases applying insurance domain and analytics expertise to meet insurers’ needs

Research & development Significant global R&D in AI and analytics for insurance

Image: used under license from shutterstock.com

Banks •  Amongst the largest in Africa •  Amongst the largest in Asia

Asian retailer •  Amongst the largest in Asia

and top 30 globally

We accept the resulting insurance risk Alignment of interest with clients on risks emerging from streamlining underwriting

Integration of auto-uw and predictive modelling Predictive models can be deployed at PoS via MRAS to drive digital predictive underwriting offers

Hong Kong

Singapore

Japan

Indonesia

Vietnam

Thailand

Dubai (Middle East)

Beijing

Malaysia

W


The four pillars of data analytics

… Creating data-driven business value requires an integrated proposition…

Integrated analytics: §  Internal §  External §  Structured § Unstructured

Data

§ Hardware §  Software

Technology

§ Data scientists &

engineers §  Actuaries §  Business people §  IT architects

People

§ Regression

models § Machine Learning

models §  Text mining

Methods

Gains stakeholder buy in

Implementable

Treats customers fairly

Risk acceptance

Auto UW

Risk Selection

Eligible Customers

Digital Sales

Distribution Method

Product Complexity

Data analytics

Pricing

Full UW

Risk Appetite

No Traditional UW

Reinsurance expertise

Simplified underwriting with minimal price increase


Why an integrated approach?

W

Conversion of analogue data into digital form that can be used by a computer

Digitisation

Manual to automated processes Automation

Data management Data Capture

Analysis

Digital transformation

Digital transformation

Prescriptive analytics

Predictive analytics

Diagnostic analytics

Descriptive analytics

What should happen?

What will happen?

Why did it happen?

What happened?

Gather data

Perform analytics

Risk assessment Risk categorisation

Targeted underwriting based on insights and risk assessment

Customised products based on customer insights

Model deployment, customised campaign design & execution

Underwriting & product

Deploy model

Campaign design & execution

Monitoring

How to get the balance right?

Munich Re accepts the resulting insurance risk

Analytics Proposition development Risk acceptance

Integrated analytics: Data-driven business value in four integrated steps


E.g. simplified underwriting, products, pricing, risk appetite?

How to get the balance right?

Define the problem

Data

s s

s

Build model Develop proposition

Deploy model Campaign design and execution

Monitoring

…We accept the resulting insurance risk

Pricing and product development Underwriting Portfolio

management Claims Sales & marketing

Which underwriting questions could be removed with minimal or no price impact?

How to identify customers with greater likelihood to purchase?

How to identify which customers are eligible for products requiring less or no underwriting? How to eliminate

underwriting requirements using external scores in underwriting?

How to use bank or physical activity data in underwriting?

How can I predict which customer smoke?

How can I predict fraudulent claims?

How can I automate claim decisions?

How can I automate aspects of experience analysis when deriving best estimates?

How to develop a product based on steps per day?

Which customers are most likely to lapse?

Can predict the time fo return to work for disability claims in payment?

How to enhance the customer’s underwriting journey focusing on immediate underwriting decisions?

Access data from various sources

Perform analytics and gather insights

s s

s

Low risk Average risk High risk

Risk selection / categorisation

Insights


Proposition development

Insights Lead generation

Standard rates with reduced underwriting and evidence requirements

Refer to underwriter

Increased STP rates

Improved take-up rates of cross and up-sell offers

Enhanced general issue (GIO) / simplified issue (SIO) product offers

Optimised underwriting rules via predictive analytics and deploy at an electronic point of sale

Reduced non-disclosure / anti-selective behaviour

Reduced lapses

Reduced claims fraud Automated claims decisions (increased claims STP)

Predict cause of claim from claims reports

Increased efficiency of experience analysis Increased accuracy of best estimates and reduced operational risk

Reduced underwriting with minimal / no increase in pricing

Reduced cost of underwriting without increasing risk

DOB Gender Smoker Income Marital Status

BMI UW Decision

7/24/1975 F N 240000 Married 30 S7/17/1969 F S 552000 Widow 22 NS7/18/1973 M N 180000 Married 30 S3/18/1971 F N 200000 Married 20 S2/27/1970 F S 352000 Married 20 S12/29/1975 F S 460000 Married 19 NS7/22/1986 M N 850000 Married 34 S7/24/1975 F N 240000 Married 30 S7/17/1969 F S 552000 Widow 22 NS7/18/1973 M N 180000 Married 30 S3/18/1971 F N 200000 Married 20 S2/27/1970 F S 352000 Married 20 S12/29/1975 F S 460000 Married 19 NS7/22/1986 M N 850000 Married 34 S

Historic records Known outcome

Build model

1

DOB 7/27/1975Gender FSmoker NIncome 240000Marital Status Married Real Weight 70

New customer record

P(Standard) 0.78

Deploy model

2

Predict outcome

3

Model

s s

s

Solution in the Cloud

Own Deployment & Integration System

Deployment options

Consume model

Model deployment, customised sales execution method

Outbound

Inbound

Hybrid

Preselect customers with offers through direct marketing channel

(lower risk)

Open to new business based on predictive variables with

offers through digital channel (higher risk)

New business profile and agent analysis

Random holdout sample

Claims experience

Trends to monitor

Risk acceptance

Example proposition outcomes

Across the whole value chain

Augmented underwriting powered by AI Asian insurers are streamlining underwriting with AI…trendsetter

1


Augmented underwriting proposition

30%

67%

3% Pain point: Number of questions Pain point:

Low STP

Pain point: Are medical evidence

requirements cost effective?

99%

0.89%

0.01%

80%

19%

1%

Pain point: Free text boxes

Pain point: Are NMLs cost-

effective?

Pain point: Was a referral unnecessary?

Potential outcomes: Increased STP

Reduced questions with minimal / no price impact

Reduced cost of underwriting

Improved customer experience

Enhanced products based on customer analytics insights Increased cost-effectiveness of medical evidence and NMLs Improved future profitability or more competitive pricing

Final decision

Final decision

Case study 2

>60% reduction in the number of referred cases (72% of all cases are currently referred by AIS) means GE can redistribute this time to other activities and eliminate opportunity cost Increased capacity to underwrite significantly larger volumes without employing more underwriting (increasing cost) and operational risk >60% reduction in the number of referred cases (72% of all cases are currently referred by AIS) enables GE to redistribute this time to other activities and eliminate opportunity cost

Improved agent and customer experience from eliminating long reflexive rules that result in referrals Significant cost reduction for GE Yes

Objective

Double STP with minimal risk increase

Reduction in application form questions with minimal increase in risk

For referred cases, assess the value of medical evidence Enhance simplified underwriting propositions by identifying customer segments that qualify for no or reduced underwriting

Human Resources Q1 OKR

Doubled STP

Less referred cases (reduced manual uw and costs)

Reduction of medical evidence for straight-through standard cases

Reduction in base questions (“BQ”) for straight-through standard cases

>60%

100%

~50%

>100%

Key results Asian insurers use AI to streamline underwriting…trendsetter

Reduction in reflexive questions for straight-through standard cases 100%

Expected business value

Better experience for applicants

Better experience for distributors

Reduced manual underwriting

Reduced operational costs

Lower NTUs

Higher sales

Faster turn around time

Reduced availability of disclosures in claims assessment

Potential business impact

Data for AI model, risk evaluation and testing

§  Additional information gathered in later stages of underwriting

§  Examples: §  Exclusions §  Reasons for decline §  Loadings on cases

§  Case data includes standard data components of each case

§  Examples: §  Age & gender §  BMI §  Sum assured §  Risk types §  Internal Flags

§  This data reflects the disclosures made on the medical conditions from the base questions

§  Examples: §  Epilepsy §  Diabetes §  High cholesterol §  Cancer

§  Base question answers §  Examples:

§  Heart conditions §  Diabetic conditions §  Family history

ANALYTICS BASE TABLE

§  Includes backend decision from underwriter §  Notes/comments from underwriter

ü  Model uses: underwriting data from an auto-underwriting engine and back-office systems

ü  Prediction of final underwriting decision

Modelling the final underwriting decision – comparing machine learning models GBM performs best and incorporates hundreds of decision trees

§  Many machine learning models are compared to identify the best performing model

§  GBM, Xgboost incorporate hundreds of single decision trees and generally are more accurate than single decision trees

25% 75%

*Area under the curve (AUC) is a measure of predictive power. A model that randomly decides the underwriting decision would produce an AUC of 0.5 and a good model over 0.85

Mod

el ty

pe

AUC* Median

FOR ILLUSTRATION ONLY

Modelling the final underwriting decision (single decision tree) Which cases may have potential to be automated as ‘accepted standard’ (STP)?

Represents > 80% of cases within which >95% received a final standard decision

Sample application form Back office flag

BMI < X

BQ1

BQ2

BQ5

BQ7

BQ4


Y N

Machine learning identifies the drivers of the final underwriting decision Which fields on your application form best predict your final underwriting decision? Which medical evidence could be eliminated without a risk cost?

Sample application form

Y N

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Var30 Var29 Var28 Var27 Var26 Var25 Var24 Var23 Var22 Var21 Var20 Var19 Var18 Var17 Var16 Var15 Var14 Var13 Var12 Var11 Var10

Var9 Var8 Var7 Var6 Var5 Var4 Var3 Var2 Var1

Relative importance

Varia

bles

Ranking the importance of case data, base questions and disclosures


Impact of reducing underwriting variables on predictive power

25% 75%

*Area under the curve (AUC) is a measure of predictive power. A model that randomly decides the underwriting decision would produce an AUC of 0.5 and a good model over 0.85

AUC*

Median

% o

f all

ques

tions

incl

uded

100%

75%

60%

50%

25%

0%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

100%

Score (probability of accepted standard in the final underwriting decision)

% o

f cas

es

STD Non-STD

How do risk implications arise from streamlining underwriting?

Based on testing set after building the model on the training data

Model score for the probability that a case is accepted standard in the final underwriting decision

•  White area indicates cases predicted by the model as “accepted standard” but should be non-standard

•  Also called false positives, i.e. cases “slipping through” incorrectly on standard rates

•  Risk implications have to be managed

•  The score is translated to an underwriting decision using a threshold above which an “accepted standard” decision is given

1 0

Score for a given case

Threshold

1 0

Risk management considerations Confusion matrix

Modelled underwriting decision

Standard Non-standard

Actual underwriter decision

Standard

Non-standard

A B

C D

A

B

C

D

True positive

False negative

False positive

True negative

Sub-standard loadings

Analysis of false positives •  Conditionally accepted •  Declined •  Exclusions

Risk management controls •  Knock out criteria •  Non-disclosure •  Monitoring:

•  New business mix •  Random holdout •  Claims

•  Ability to re-train models

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 2% 4% 6% 8% 10%

Risk implications of streamlining underwriting Trade off between STP, number of underwriting questions and the score threshold

Current STP

50% STP 100 variables 0.8% “slip-through”

80% STP 100 variables 2.6% “slip-through”

Number of underwriting variables

10 100 All (1000) S

TP

% of cases incorrectly predicted as standard

Risk implications of streamlining underwriting Using actuarial, underwriting and claims expertise to understand risks posed by false positives

Other conditions?

% o

f slip

thro

ugh

case

s

Declined Postponed Conditionally Accepted

No loading

Loading

Loading

No loading

No

excl

usio

n E

xclu

sion

Breakdown of false positives Conditionally accepted slip through cases Pricing assumptions

?

?

?

?

Risk implications of streamlining underwriting Trade off between STP, number of underwriting questions and the score threshold

STP

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 2% 4% 6% 8% 10%

Current STP

50% STP 100 variables 0.45% loading

80% STP 100 variables 1.5% loading

Percentage loading to qx (best estimate)

Number of underwriting variables

10 100 All

Deployment process and considerations Integration with Munich Re’s automated underwriting engine

Agent application

§  Integration of predictive model with underwriting engine and back office systems

§  Availability of model inputs at the required stages

§  Offline/online agent use of engine

§  Knock out by product/risk types, demographics, etc.

§  Monitoring of model performance and risk implications

§  Model maintenance

Online Offline

One decision

Explaining a single AI underwriting decision

Var 1 Var 2 Var 3 Var 4 Var 5 Var 6 Var 7 Var 8



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... …

… … … … … …

… … … … … … … …

Q6

Total shap value

Shap value*

… … BMI

Reducing / replacing traditional underwriting with external data

3

Uses of bank and retail data

§  Frictionless underwriting: §  Identify which customers are eligible for reduced underwriting and minimal / no price increase

§  Assess the degree to which bank data can replace some/all traditional underwriting which enables an improved customer experience, increased take up and reduced cost of underwriting

§  Increased STP (automation): predictive models could avoid many referrals

§  Propensity to buy: §  Enables targeted digital offers with increased take up

§  Bank customer segmentation enables product offers that match customer needs

§  Competitive pricing: less (friction in) underwriting with no / minimal price increase

§  Data-driven product solutions: §  Clustering of bank customers creates segments for matching / designing relevant products

§  Combined bank and insurance products that evolve with customers’ life stages (events) and are triggered and streamlined using bank and other data

Considerations in achieving frictionless underwriting Using bank data can potentially avoid a price increase when reducing traditional underwriting

Price

Level of underwriting friction e.g. Number of questions

GIO (Very little is known about the customer)

SIO (Little is known about the customer)

Fully underwritten (A lot is known about the customer)

Better than fully underwritten (A lot is known about the customer and incorporates bank data)

76% 78% 80% 82% 84% 86% 88% 90% 92%

Top 5 Top 15 Top 20 Top 40 Top 100

Acc

urac

y

Number of underwriting questions (As proxy for underwriting friction)

GIO

SIO Full u/w

Replacing traditional u/w with bank data (A lot is known about the customer)

Thank you Lee Sarkin Head: Regional Analytics Centre (Life & Health) Asia-Pacific, Middle East and Africa [email protected]

ai and advanced analytics: augmented underwriting · the four pillars of data analytics ... build...

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